@InProceedings{ture-jojic:2017:EMNLP2017,
  author    = {Ture, Ferhan  and  Jojic, Oliver},
  title     = {No Need to Pay Attention: Simple Recurrent Neural Networks Work!},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2866--2872},
  abstract  = {First-order factoid question answering assumes that the question can be
	answered by a single fact in a knowledge base (KB). While this does not seem
	like a challenging task, many recent attempts that apply either complex
	linguistic reasoning or deep neural networks achieve 65\%--76\% accuracy on
	benchmark
	sets. Our approach formulates the task as two machine learning problems:\
	detecting the entities in the question, and classifying the question as one of
	the relation types in the KB. We train a recurrent neural network to solve each
	problem. On the SimpleQuestions dataset, our approach yields substantial
	improvements over previously published results --- even neural networks based
	on much more complex architectures. The simplicity of our approach also has
	practical advantages, such as efficiency and modularity, that are valuable
	especially in an industry setting. In fact, we present a preliminary analysis
	of the performance of our model on real queries from Comcast's X1 entertainment
	platform with millions of users every day.},
  url       = {https://www.aclweb.org/anthology/D17-1307}
}